numpy.minimum¶

numpy.minimum(x1,x2,/,out=None,*,where=True,casting='same_kind',order='K',dtype=None,subok=True[,signature,extobj]) = <ufunc 'minimum'>¶

Element-wise minimum of array elements.

Compare two arrays and returns a new array containing the element-wiseminima. If one of the elements being compared is a NaN, then thatelement is returned. If both elements are NaNs then the first isreturned. The latter distinction is important for complex NaNs, whichare defined as at least one of the real or imaginary parts being a NaN.The net effect is that NaNs are propagated.

Parameters:

x1, x2 : array_like The arrays holding the elements to be compared. They must havethe same shape, or shapes that can be broadcast to a single shape. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must havea shape that the inputs broadcast to. If not provided orNone,a freshly-allocated array is returned. A tuple (possible only as akeyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, valuesof False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.

Returns:

y : ndarray or scalar The minimum ofx1 andx2, element-wise. Returns scalar ifbothx1 andx2 are scalars.

See also

maximum

Element-wise maximum of two arrays, propagates NaNs.

fmin

Element-wise minimum of two arrays, ignores NaNs.

amin

The minimum value of an array along a given axis, propagates NaNs.

nanmin

The minimum value of an array along a given axis, ignores NaNs.

fmax,amax,nanmax

Notes

The minimum is equivalent tonp.where(x1<=x2,x1,x2) whenneither x1 nor x2 are NaNs, but it is faster and does properbroadcasting.

Examples

>>>np.minimum([2,3,4],[1,5,2])array([1, 3, 2])

>>>np.minimum(np.eye(2),[0.5,2])# broadcastingarray([[ 0.5,  0. ],       [ 0. ,  1. ]])

>>>np.minimum([np.nan,0,np.nan],[0,np.nan,np.nan])array([ NaN,  NaN,  NaN])>>>np.minimum(-np.Inf,1)-inf